The subject matter herein relates generally to systems and methods for modeling electrical activity of an anatomical structure, and more particularly, to systems and methods for modeling cardiac electrical activity.
Electrocardiographic (ECG) data represents the combined electrical activity of the cells of the heart, also referred to as cardiac cells. The cardiac cells experience electrical impulses called action potentials that cause the cardiac cells to contract after stimulation. The cardiac cells in different regions and layers (i.e., cardiac cells having different spatial positions in the heart) may experience different types of action potentials at different times during a cardiac cycle. The combined electrical activity of the cardiac cells during the cardiac cycle may be detected as a waveform showing electrical potential over time. For example, one conventional method of collecting ECG data uses ten electrodes that are placed on the skin of a patient in predetermined locations. Each cardiac cycle may be recorded as a PQRST waveform or complex, where the letters P, Q, R, S, and T represent different waves or deflections in the PQRST waveform. Generally, a P-wave corresponds to activity in the atria, a QRS complex represents the electrical activation of the ventricles, and a T-wave represents electrical recovery or a recharge phase of the ventricles.
The PQRST waveform may be analyzed to identify waveform features (e.g., QT interval, shape of T-wave, ST segment, T peak to T end (TpTe) interval) that may be associated with cardiac conditions. For example, a prolonged QT interval has been associated with potentially life threatening medical conditions, such as cardiac arrhythmia. As such, if a pharmaceutical company discovers that a drug under study may cause a prolonged QT interval, the company may cease its research of that drug. However, the QT interval has several limitations. First, the QT interval may not be highly correlative with some severe medical conditions. For example, a drug may affect the electrical activity of cardiac cells in certain regions of the heart such that the electrical activity of the cardiac cycle is ultimately recorded by an ECG monitor as having a prolonged QT interval. However, the affected cardiac cells may not represent a threat to the health of the patient. As such, viable and potentially helpful drugs may be excluded from further study due to erroneous concerns over the drug's safety. Second, the QT interval is dependent upon the heart rate and, consequently, the QT interval is usually corrected before analysis, which introduces another level of error. Also, the QT interval can be difficult to measure and analyze.
Accordingly, researchers and health practitioners are seeking alternative waveform features that may better identify cardiac conditions of interest. However, some current methods of identifying such waveform features include obtaining ECG data from patients and, after diagnosing a cardiac condition of the patients or recording a final event (e.g., heart attack), determining if any waveform features are associated with the cardiac condition or the final event. Such methods may be expensive and time-consuming. Other methods include using cell or tissue models that simulate the electrical activity of the cardiac cells. However, these methods may not model the whole heart, and may not determine the ultimate waveforms that may be detected through, for example, the conventional twelve lead ECG and/or do not provide a user-friendly format for analyzing and investigating waveform features.
Also, another problem faced by researchers and health practitioners may be the PQRST waveform itself. Although useful in identifying and determining some cardiac conditions, the current standard arrangement of ten electrodes provides only one view of the electrical activity of the heart. Many other arrangements of electrodes may be used to provide more easily identifiable waveform features that are associated with cardiac conditions. However, the cost in finding such waveform features may be prohibitive.
Accordingly, there is a need for systems and methods that identify waveform features associated with health conditions of interest. There is also a need for systems and methods that determine arrangements of electrodes that may facilitate detecting such waveform features. Furthermore, there is a need for user-friendly systems and methods for modeling electrical activity of an anatomical structure